Oil Market Efficiency Under a Machine Learning Perspective

Forecasting, Forthcoming

18 Pages Posted: 26 Nov 2018

See all articles by Athanasia Dimitriadou

Athanasia Dimitriadou

Democritus University of Thrace

Periklis Gogas

Democritus University of Thrace - Department of Economics

Theophilos Papadimitriou

Department of Economics, Democritus University of Thrace

Vasilios Plakandaras

Democritus University of Thrace

Date Written: September 15, 2018

Abstract

Forecasting commodities and especially oil prices has attracted significant research interest, often concluding that oil prices are not easy to forecast and implying an efficient market. In this paper, we revisit the efficient market hypothesis of the oil market attempting to forecast the West Texas Intermediate oil prices under a machine learning framework. In doing so, we compile a dataset of 38 potential explanatory variables often used in the relevant literature and through a selection process we build forecasting models that use past oil prices, refined oil products and exchange rates as independent variables. Our empirical findings suggest that the Support Vector Machines (SVM) model coupled with the non-linear Radial Basis Function kernel outperforms the linear SVM and the traditional logistic regression (LOGIT) models. Moreover, we provide evidence that points to the rejection of even the weak form of efficiency in the oil market.

Keywords: Oil Prices, Forecasting, Machine Learning, Support Vector Machines

JEL Classification: C22, C53

Suggested Citation

Dimitriadou, Athanasia and Gogas, Periklis and Papadimitriou, Theophilos and Plakandaras, Vasilios, Oil Market Efficiency Under a Machine Learning Perspective (September 15, 2018). Forecasting, Forthcoming . Available at SSRN: https://ssrn.com/abstract=3275923 or http://dx.doi.org/10.2139/ssrn.3275923

Athanasia Dimitriadou

Democritus University of Thrace ( email )

Vas. Sofias 12, Building 1, Production & Managemen
Office 303, 3rd floor
Xanthi, Xanthi 68100
Greece

Periklis Gogas

Democritus University of Thrace - Department of Economics ( email )

Komotini, 69100
Greece

HOME PAGE: http://www.econ.duth.gr/personel/dep/gkogkas/index.en.shtml

Theophilos Papadimitriou

Department of Economics, Democritus University of Thrace ( email )

University Campus
Komotini, 69100
Greece

HOME PAGE: http://econ.duth.gr/author/papadimi/

Vasilios Plakandaras (Contact Author)

Democritus University of Thrace ( email )

University Campus
Komotini, 69100
Greece

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